






"""simple docstring"""


import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler




class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	int     , UpperCAmelCase							:	UNetaDModel     , UpperCAmelCase							:	UNetaDModel     , UpperCAmelCase							:	DDPMScheduler     , UpperCAmelCase							:	List[str]     , )	->       int:
											super().__init__()
											lowerCAmelCase   :str						=			value_function
											lowerCAmelCase   :Optional[Any]						=			unet
											lowerCAmelCase   :List[Any]						=			scheduler
											lowerCAmelCase   :str						=			env
											lowerCAmelCase   :Optional[int]						=			env.get_dataset()
											lowerCAmelCase   :Union[str, Any]						=			{}
											for key in self.data.keys():
																	try:
																							lowerCAmelCase   :Tuple						=			self.data[key].mean()
																	except:  # noqa: E722
																							pass
											lowerCAmelCase   :int						=			{}
											for key in self.data.keys():
																	try:
																							lowerCAmelCase   :Tuple						=			self.data[key].std()
																	except:  # noqa: E722
																							pass
											lowerCAmelCase   :List[str]						=			env.observation_space.shape[0]
											lowerCAmelCase   :Tuple						=			env.action_space.shape[0]
					def        UpperCAmelCase__ (							self							:	Optional[int]     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	Optional[Any]   )	->       Dict:
											return (x_in - self.means[key]) / self.stds[key]
					def        UpperCAmelCase__ (							self							:	Tuple     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Tuple   )	->       Dict:
											return x_in * self.stds[key] + self.means[key]
					def        UpperCAmelCase__ (							self							:	List[Any]     , UpperCAmelCase							:	str   )	->       List[str]:
											if type(UpperCAmelCase   ) is dict:
																	return {k: self.to_torch(UpperCAmelCase   ) for k, v in x_in.items()}
											elif torch.is_tensor(UpperCAmelCase   ):
																	return x_in.to(self.unet.device   )
											return torch.tensor(UpperCAmelCase     , device=self.unet.device   )
					def        UpperCAmelCase__ (							self							:	Tuple     , UpperCAmelCase							:	Any     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Union[str, Any]   )	->       Optional[int]:
											for key, val in cond.items():
																	lowerCAmelCase   :Dict						=			val.clone()
											return x_in
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	str     , UpperCAmelCase							:	Optional[Any]   )	->       Union[str, Any]:
											lowerCAmelCase   :List[str]						=			x.shape[0]
											lowerCAmelCase   :List[Any]						=			None
											for i in tqdm.tqdm(self.scheduler.timesteps   ):
																	# create batch of timesteps to pass into model
																	lowerCAmelCase   :Tuple						=			torch.full((batch_size,)     , UpperCAmelCase     , device=self.unet.device     , dtype=torch.long   )
																	for _ in range(UpperCAmelCase   ):
																							with torch.enable_grad():
																													x.requires_grad_()

																													# permute to match dimension for pre-trained models
																													lowerCAmelCase   :int						=			self.value_function(x.permute(0     , 2     , 1   )     , UpperCAmelCase   ).sample
																													lowerCAmelCase   :Tuple						=			torch.autograd.grad([y.sum()]     , [x]   )[0]

																													lowerCAmelCase   :str						=			self.scheduler._get_variance(UpperCAmelCase   )
																													lowerCAmelCase   :str						=			torch.exp(0.5 * posterior_variance   )
																													lowerCAmelCase   :str						=			model_std * grad

																							lowerCAmelCase   :Optional[int]						=			0
																							lowerCAmelCase   :List[str]						=			x.detach()
																							lowerCAmelCase   :Dict						=			x + scale * grad
																							lowerCAmelCase   :Optional[int]						=			self.reset_xa(UpperCAmelCase     , UpperCAmelCase     , self.action_dim   )

																	lowerCAmelCase   :str						=			self.unet(x.permute(0     , 2     , 1   )     , UpperCAmelCase   ).sample.permute(0     , 2     , 1   )

																	# TODO: verify deprecation of this kwarg
																	lowerCAmelCase   :Optional[int]						=			self.scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , predict_epsilon=UpperCAmelCase   )['prev_sample']

																	# apply conditions to the trajectory (set the initial state)
																	lowerCAmelCase   :str						=			self.reset_xa(UpperCAmelCase     , UpperCAmelCase     , self.action_dim   )
																	lowerCAmelCase   :List[Any]						=			self.to_torch(UpperCAmelCase   )
											return x, y
					def __call__(							self							:	str     , UpperCAmelCase							:	Optional[Any]     , UpperCAmelCase							:	List[str]=64     , UpperCAmelCase							:	Tuple=32     , UpperCAmelCase							:	Optional[int]=2     , UpperCAmelCase							:	Optional[int]=0.1   )	->       Optional[int]:
											# normalize the observations and create  batch dimension
											lowerCAmelCase   :List[str]						=			self.normalize(UpperCAmelCase     , 'observations'   )
											lowerCAmelCase   :Optional[int]						=			obs[None].repeat(UpperCAmelCase     , axis=0   )

											lowerCAmelCase   :Optional[Any]						=			{0: self.to_torch(UpperCAmelCase   )}
											lowerCAmelCase   :Any						=			(batch_size, planning_horizon, self.state_dim + self.action_dim)

											# generate initial noise and apply our conditions (to make the trajectories start at current state)
											lowerCAmelCase   :Dict						=			randn_tensor(UpperCAmelCase     , device=self.unet.device   )
											lowerCAmelCase   :List[Any]						=			self.reset_xa(UpperCAmelCase     , UpperCAmelCase     , self.action_dim   )
											lowerCAmelCase   :Any						=			self.to_torch(UpperCAmelCase   )

											# run the diffusion process
											lowerCAmelCase	, lowerCAmelCase   :int						=			self.run_diffusion(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   )

											# sort output trajectories by value
											lowerCAmelCase   :Tuple						=			y.argsort(0     , descending=UpperCAmelCase   ).squeeze()
											lowerCAmelCase   :Union[str, Any]						=			x[sorted_idx]
											lowerCAmelCase   :List[str]						=			sorted_values[:, :, : self.action_dim]
											lowerCAmelCase   :Dict						=			actions.detach().cpu().numpy()
											lowerCAmelCase   :List[Any]						=			self.de_normalize(UpperCAmelCase     , key='actions'   )

											# select the action with the highest value
											if y is not None:
																	lowerCAmelCase   :Dict						=			0
											else:
																	# if we didn't run value guiding, select a random action
																	lowerCAmelCase   :List[Any]						=			np.random.randint(0     , UpperCAmelCase   )

											lowerCAmelCase   :Tuple						=			denorm_actions[selected_index, 0]
											return denorm_actions







"""simple docstring"""


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase						)
class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	List[Any]     , *UpperCAmelCase							:	Any     , **UpperCAmelCase							:	Tuple   )	->       Optional[int]:
											super().__init__(*UpperCAmelCase     , **UpperCAmelCase   )
											requires_backends(self     , 'vision'   )
											self.check_model_type(
											    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	str=None     , UpperCAmelCase							:	str=None     , UpperCAmelCase							:	List[str]=None   )	->       Tuple:
											lowerCAmelCase   :Optional[Any]						=			{}
											lowerCAmelCase   :int						=			{}

											if prompt is not None:
																	lowerCAmelCase   :Any						=			prompt

											if generate_kwargs is not None:
																	lowerCAmelCase   :List[Any]						=			generate_kwargs
											if max_new_tokens is not None:
																	if "generate_kwargs" not in forward_kwargs:
																							lowerCAmelCase   :Any						=			{}
																	if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																							raise ValueError(
																							    '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
																							    ' please use only one'   )
																	lowerCAmelCase   :Union[str, Any]						=			max_new_tokens
											return preprocess_params, forward_kwargs, {}
					def __call__(							self							:	Tuple     , UpperCAmelCase							:	Union[str, List[str], "Image.Image", List["Image.Image"]]     , **UpperCAmelCase							:	Any   )	->       Optional[int]:
											return super().__call__(UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	Any=None   )	->       str:
											lowerCAmelCase   :Dict						=			load_image(UpperCAmelCase   )

											if prompt is not None:
																	if not isinstance(UpperCAmelCase     , UpperCAmelCase   ):
																							raise ValueError(
																							    f"""Received an invalid text input, got - {type(UpperCAmelCase   )} - but expected a single string. """
																							    'Note also that one single text can be provided for conditional image to text generation.'   )

																	lowerCAmelCase   :List[str]						=			self.model.config.model_type

																	if model_type == "git":
																							lowerCAmelCase   :Any						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )
																							lowerCAmelCase   :Union[str, Any]						=			self.tokenizer(text=UpperCAmelCase     , add_special_tokens=UpperCAmelCase   ).input_ids
																							lowerCAmelCase   :Optional[int]						=			[self.tokenizer.cls_token_id] + input_ids
																							lowerCAmelCase   :Union[str, Any]						=			torch.tensor(UpperCAmelCase   ).unsqueeze(0   )
																							model_inputs.update({'input_ids': input_ids}   )

																	elif model_type == "pix2struct":
																							lowerCAmelCase   :Optional[Any]						=			self.image_processor(images=UpperCAmelCase     , header_text=UpperCAmelCase     , return_tensors=self.framework   )

																	elif model_type != "vision-encoder-decoder":
																							# vision-encoder-decoder does not support conditional generation
																							lowerCAmelCase   :int						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )
																							lowerCAmelCase   :Optional[Any]						=			self.tokenizer(UpperCAmelCase     , return_tensors=self.framework   )
																							model_inputs.update(UpperCAmelCase   )

																	else:
																							raise ValueError(f"""Model type {model_type} does not support conditional text generation"""   )

											else:
																	lowerCAmelCase   :str						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )

											if self.model.config.model_type == "git" and prompt is None:
																	lowerCAmelCase   :Union[str, Any]						=			None

											return model_inputs
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Any=None   )	->       int:
											# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
											# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
											if (
											    "input_ids" in model_inputs
											    and isinstance(model_inputs['input_ids']     , UpperCAmelCase   )
											    and all(x is None for x in model_inputs['input_ids']   )
											):
																	lowerCAmelCase   :Optional[int]						=			None

											if generate_kwargs is None:
																	lowerCAmelCase   :Optional[int]						=			{}
											# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
											#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
											#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
											#  in the `_prepare_model_inputs` method.
											lowerCAmelCase   :Optional[int]						=			model_inputs.pop(self.model.main_input_name   )
											lowerCAmelCase   :Any						=			self.model.generate(UpperCAmelCase     , **UpperCAmelCase     , **UpperCAmelCase   )
											return model_outputs
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	Union[str, Any]   )	->       Any:
											lowerCAmelCase   :str						=			[]
											for output_ids in model_outputs:
																	lowerCAmelCase   :Optional[int]						=			{
																	    'generated_text': self.tokenizer.decode(
																	        UpperCAmelCase     , skip_special_tokens=UpperCAmelCase     , )
																	}
																	records.append(UpperCAmelCase   )
											return records